Intelligent Buildings: A Mind Is a Terrible Thing to Waste

A classic advertising campaign once told us that a mind is a terrible thing to waste. This is true for buildings as well -- more and more buildings these days have “brains.” Smart meters that measure energy consumption, sensors that detect when people are in a room and heating, air conditioning and lighting management systems are making the walls around us much more intelligent.

This means that buildings have the potential to play a vital role in that web of interconnected systems that make up the Internet of Things. However, the intelligence quotient of buildings is being squandered: most of the built environment fails to leverage, or even access, the lowest common denominator of energy performance -- building consumption data.

However, signs point to significant change for more intelligent buildings in the years to come. An increased focus on energy efficiency is driving a growing appetite for more robust building usage data, which in turn reveals massive insights into how a building functions and how its performance can be improved over time. To assess this opportunity, let’s consider what’s going on behind the utility meters.

Not Big Data, Deep Data

Building energy efficiency does not need a Big Data solution as much as it needs a Deep Data solution. Big data is about sophisticated data processing techniques to uncover information from large, multisource data sets that contain both structured and unstructured data. On the other hand, the complexity of commercial structures places a premium on solutions that provide deep, building-specific analysis and customized, actionable answers. These commercial structures all have between 8,760 and 35,040 annual meter data points available, even before large multi-source data sets are found. This rich, time-sensitive and constantly changing information doesn't necessarily pose the same challenges that massive amounts of data do.

This is crucial: without the need for expensive devices and integration, deep data analytics can reveal highly relevant insights into how each building uses energy. Opportunities are instantly unlocked for not just cost savings but also for changing the behavior of the people who occupy and operate a particular building every day.

Private Financing Equals Massive Catalyst

Energy efficiency is the only industry I can think of where people spend $20-30 billion a year without data to back up decision making for efficiency projects. In 2007, the George W. Bush White House administration allocated $5 billion in smart meter spending alone; in early 2013, the Obama administration announced an aggressive goal to make the U.S. 20 percent more energy efficient by 2030. The U.S. General Services Administration is using software to meet a two percent energy use reduction goal by 2015 (for a portfolio of buildings of a hundred million square feet), and cities like Washington, D.C. are in a race to the top for efficiency. D.C. aims to hit 20 percent energy savings within 20 months.

What’s missing from this picture of enthusiasm, commitment and innovation in energy efficiency is the private sector—specifically, financing from the private sector to spur faster adoption of energy efficiency efforts. Data is the key that will unlock private investment in this area: once building usage data becomes more standardized in terms of information inputs and intelligence outputs, private money will become a catalyst for even faster adoption of energy efficiency projects, especially in the commercial sector.

Don’t Look Across the Road for Energy Intelligence

Historically, energy efficiency strategies were designed based on comparisons to buildings with a similar size and use-profile. In today’s world, however, people expect strategies that are tailored for their own building—in other words, strategies that will actually work for them. When assessing a building’s energy profile, people don’t really care about data associated with nearby buildings. Buildings have their own unique energy “fingerprints” and these fingerprints dictate the efficiency decisions that should be made. Focusing on specific building data is another element of deep data—with increased access to detailed consumption data and advanced analytics, specific building energy inefficiencies become apparent earlier and faster, making it simpler for managers to tackle the problems at hand and identify a more efficient solution for each building.

What’s Next?

In the coming year, these trends will converge globally. Mandates for energy efficiency via utility company incentives and other mechanisms have been in place in the U.S. for years, but as buildings get smarter and start transforming their usage data into real intelligence, the commercial building sector will transform. At the same time, in other parts of the world such as Europe, shifts in utility regulation will also change the energy efficiency game as countries wrangle with how to unlock efficiency and cost savings from within a rapidly changing building industry.

Deep data can be a driver for incredible change that will foster not just cost savings but also a true catalyst for environmental gains across every building and every city in the world.